Comparing Medical Comorbidities Between Opioid and Cocaine Users: A Data Mining Approach

Document Type : Original Article


Yong-Mi Kim, PhD Associate Professor School of Library and Information Studies University of Oklahoma Schusterman Center 4502 East 41st Street Tulsa, OK 74135


Background: Prescription drug monitoring programs (PDMPs) are instrumental in controlling opioid misuse,
but opioid users have increasingly shifted to cocaine, creating a different set of medical problems. While
opioid use results in multiple medical comorbidities, findings of the existing studies reported single
comorbidities rather than a set, and furthermore, those findings are often conflicting because of the lack of
controlling for other substances in the analysis when combined use of substance creates synergistic effects.
On the other hand, the findings from cocaine use are mainly related to kidney and heart problems, which lack
specificity. Because medical comorbidities from opioid and cocaine use are very different, it is imperative to
investigate medical comorbidities from opioids and cocaine in order to minimize negative effects from
PDMPs. Therefore, this study attempts to discover sets of medical comorbidities from opioid and cocaine use
by controlling for other substances in the analysis.
Methods: A data mining technique, association rule mining algorithm, was employed to discover sets of
medical comorbidities using electronic medical records. This method is ideal to discover co-occurring
medical comorbidities.
Findings: Opioid use was associated with a set of [high diastolic blood pressure (DBP), abnormal specific
gravity], [high body mass index (BMI), low blood gas] among others. Cocaine use correlated with [high
creatine kinase (CK), high blood urea nitrogen (BUN)], [high CK, cardiopulmonary] among others.
Conclusion: The findings of this study addresses some of the conflicting findings by eliminating multidrug
and reports sets of medical comorbidities from opioid and cocaine use


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